Overview

Dataset statistics

Number of variables22
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory248.2 KiB
Average record size in memory127.1 B

Variable types

Numeric15
Boolean7

Variable descriptions

idID
battery_powerTotal energy a battery can store in one time measured in mAh
has_bluetoothHas bluetooth or not
clock_speedspeed at which microprocessor executes instructions
has_dual_simHas dual sim support or not
front_cam_resolutionFront Camera mega pixels
has_four_gHas 4G or not. 1 = yes , 0 = no
int_memoryinternal Memory in Gigabytes
mobile_depthMobile Depth in cm
mobile_widthWeight of mobile phone
number_of_coresNumber of cores of processor
primary_cam_resolutionPrimary Camera mega pixels
px_heightPixel Resolution Height
px_widthPixel Resolution Width
ramRandom Access Memory in Mega Bytes
screen_heightScreen Height of mobile in cm
screen_weightScreen Width of mobile in cm
talk_timelongest time that a single battery charge will last when you are
has_three_gHas 3G or not
has_touch_screenHas touch screen or not, 1 = yes, 0 = no
has_wifiHas wifi or not
is_expensiveThis is the target variable with indicating if the mobile phone got a high price. 1 = yes, 0 = no

Warnings

id is uniformly distributed Uniform
id has unique values Unique
front_cam_resolution has 474 (23.7%) zeros Zeros
primary_cam_resolution has 101 (5.1%) zeros Zeros
screen_weight has 180 (9.0%) zeros Zeros

Reproduction

Analysis started2021-05-02 02:57:02.995178
Analysis finished2021-05-02 02:57:35.008495
Duration32.01 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

ID

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean999.5
Minimum0
Maximum1999
Zeros1
Zeros (%)< 0.1%
Memory size15.8 KiB
2021-05-02T10:57:35.145329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99.95
Q1499.75
median999.5
Q31499.25
95-th percentile1899.05
Maximum1999
Range1999
Interquartile range (IQR)999.5

Descriptive statistics

Standard deviation577.4945887
Coefficient of variation (CV)0.5777834805
Kurtosis-1.2
Mean999.5
Median Absolute Deviation (MAD)500
Skewness0
Sum1999000
Variance333500
MonotocityStrictly increasing
2021-05-02T10:57:35.303596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19991
 
0.1%
13401
 
0.1%
13141
 
0.1%
13161
 
0.1%
13181
 
0.1%
13201
 
0.1%
13221
 
0.1%
13241
 
0.1%
13261
 
0.1%
13281
 
0.1%
Other values (1990)1990
99.5%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
19991
0.1%
19981
0.1%
19971
0.1%
19961
0.1%
19951
0.1%
19941
0.1%
19931
0.1%
19921
0.1%
19911
0.1%
19901
0.1%

battery_power
Real number (ℝ≥0)

Total energy a battery can store in one time measured in mAh

Distinct1094
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.5185
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:35.471121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile570.95
Q1851.75
median1226
Q31615.25
95-th percentile1930.15
Maximum1998
Range1497
Interquartile range (IQR)763.5

Descriptive statistics

Standard deviation439.4182061
Coefficient of variation (CV)0.3547934133
Kurtosis-1.224143883
Mean1238.5185
Median Absolute Deviation (MAD)382
Skewness0.03189847179
Sum2477037
Variance193088.3598
MonotocityNot monotonic
2021-05-02T10:57:35.609122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15896
 
0.3%
6186
 
0.3%
18726
 
0.3%
13795
 
0.2%
13105
 
0.2%
10635
 
0.2%
8325
 
0.2%
14145
 
0.2%
14135
 
0.2%
18075
 
0.2%
Other values (1084)1947
97.4%
ValueCountFrequency (%)
5012
 
0.1%
5022
 
0.1%
5033
0.1%
5045
0.2%
5061
 
0.1%
5072
 
0.1%
5083
0.1%
5091
 
0.1%
5103
0.1%
5114
0.2%
ValueCountFrequency (%)
19981
 
0.1%
19971
 
0.1%
19962
0.1%
19952
0.1%
19943
0.1%
19931
 
0.1%
19922
0.1%
19914
0.2%
19892
0.1%
19881
 
0.1%

has_bluetooth
Boolean

Has bluetooth or not

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1010 
True
990 
ValueCountFrequency (%)
False1010
50.5%
True990
49.5%
2021-05-02T10:57:35.728122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

clock_speed
Real number (ℝ≥0)

speed at which microprocessor executes instructions

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.52225
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:35.804126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.2
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.8160042089
Coefficient of variation (CV)0.5360513772
Kurtosis-1.323417222
Mean1.52225
Median Absolute Deviation (MAD)0.8
Skewness0.1780841203
Sum3044.5
Variance0.6658628689
MonotocityNot monotonic
2021-05-02T10:57:35.921221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5413
20.6%
2.885
 
4.2%
2.378
 
3.9%
1.676
 
3.8%
2.176
 
3.8%
2.574
 
3.7%
0.674
 
3.7%
1.470
 
3.5%
1.368
 
3.4%
267
 
3.4%
Other values (16)919
46.0%
ValueCountFrequency (%)
0.5413
20.6%
0.674
 
3.7%
0.764
 
3.2%
0.858
 
2.9%
0.958
 
2.9%
161
 
3.0%
1.151
 
2.5%
1.256
 
2.8%
1.368
 
3.4%
1.470
 
3.5%
ValueCountFrequency (%)
328
 
1.4%
2.962
3.1%
2.885
4.2%
2.755
2.8%
2.655
2.8%
2.574
3.7%
2.458
2.9%
2.378
3.9%
2.259
2.9%
2.176
3.8%

has_dual_sim
Boolean

Has dual sim support or not

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1019 
False
981 
ValueCountFrequency (%)
True1019
50.9%
False981
49.0%
2021-05-02T10:57:36.004799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

front_cam_resolution
Real number (ℝ≥0)

ZEROS

Front Camera mega pixels

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3095
Minimum0
Maximum19
Zeros474
Zeros (%)23.7%
Memory size15.8 KiB
2021-05-02T10:57:36.076551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.341443748
Coefficient of variation (CV)1.007412402
Kurtosis0.2770763246
Mean4.3095
Median Absolute Deviation (MAD)3
Skewness1.019811411
Sum8619
Variance18.84813382
MonotocityNot monotonic
2021-05-02T10:57:36.166976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0474
23.7%
1245
12.2%
2189
 
9.4%
3170
 
8.5%
5139
 
7.0%
4133
 
6.7%
6112
 
5.6%
7100
 
5.0%
978
 
3.9%
877
 
3.9%
Other values (10)283
14.1%
ValueCountFrequency (%)
0474
23.7%
1245
12.2%
2189
 
9.4%
3170
 
8.5%
4133
 
6.7%
5139
 
7.0%
6112
 
5.6%
7100
 
5.0%
877
 
3.9%
978
 
3.9%
ValueCountFrequency (%)
191
 
0.1%
1811
 
0.5%
176
 
0.3%
1624
 
1.2%
1523
 
1.1%
1420
 
1.0%
1340
2.0%
1245
2.2%
1151
2.5%
1062
3.1%

has_four_g
Boolean

Has 4G or not. 1 = yes , 0 = no

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1043 
False
957 
ValueCountFrequency (%)
True1043
52.1%
False957
47.9%
2021-05-02T10:57:36.250905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

int_memory
Real number (ℝ≥0)

internal Memory in Gigabytes

Distinct63
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.0465
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:36.338002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median32
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.14571496
Coefficient of variation (CV)0.5662307882
Kurtosis-1.21607403
Mean32.0465
Median Absolute Deviation (MAD)16
Skewness0.05788932785
Sum64093
Variance329.2669712
MonotocityNot monotonic
2021-05-02T10:57:36.458042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2747
 
2.4%
1445
 
2.2%
1645
 
2.2%
242
 
2.1%
5742
 
2.1%
740
 
2.0%
4240
 
2.0%
4439
 
1.9%
3039
 
1.9%
637
 
1.8%
Other values (53)1584
79.2%
ValueCountFrequency (%)
242
2.1%
325
1.2%
420
1.0%
536
1.8%
637
1.8%
740
2.0%
837
1.8%
935
1.8%
1036
1.8%
1134
1.7%
ValueCountFrequency (%)
6431
1.6%
6330
1.5%
6221
1.1%
6127
1.4%
6027
1.4%
5918
0.9%
5836
1.8%
5742
2.1%
5627
1.4%
5529
1.5%

mobile_depth
Real number (ℝ≥0)

Mobile Depth in cm

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50175
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:36.574936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.5
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.2884155496
Coefficient of variation (CV)0.5748192319
Kurtosis-1.274348884
Mean0.50175
Median Absolute Deviation (MAD)0.3
Skewness0.08908200979
Sum1003.5
Variance0.08318352926
MonotocityNot monotonic
2021-05-02T10:57:36.657511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1320
16.0%
0.2213
10.7%
0.8208
10.4%
0.5205
10.2%
0.7200
10.0%
0.3199
10.0%
0.9195
9.8%
0.6186
9.3%
0.4168
8.4%
1106
 
5.3%
ValueCountFrequency (%)
0.1320
16.0%
0.2213
10.7%
0.3199
10.0%
0.4168
8.4%
0.5205
10.2%
0.6186
9.3%
0.7200
10.0%
0.8208
10.4%
0.9195
9.8%
1106
 
5.3%
ValueCountFrequency (%)
1106
 
5.3%
0.9195
9.8%
0.8208
10.4%
0.7200
10.0%
0.6186
9.3%
0.5205
10.2%
0.4168
8.4%
0.3199
10.0%
0.2213
10.7%
0.1320
16.0%

mobile_wt
Real number (ℝ≥0)

Distinct121
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.249
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:36.777774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median141
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.3996549
Coefficient of variation (CV)0.2524057562
Kurtosis-1.210376474
Mean140.249
Median Absolute Deviation (MAD)31
Skewness0.006558157429
Sum280498
Variance1253.135567
MonotocityNot monotonic
2021-05-02T10:57:36.915673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18228
 
1.4%
18527
 
1.4%
10127
 
1.4%
14626
 
1.3%
19926
 
1.3%
8825
 
1.2%
10525
 
1.2%
19825
 
1.2%
8924
 
1.2%
14523
 
1.1%
Other values (111)1744
87.2%
ValueCountFrequency (%)
8021
1.1%
8113
0.7%
8215
0.8%
8319
0.9%
8417
0.9%
8513
0.7%
8619
0.9%
8715
0.8%
8825
1.2%
8924
1.2%
ValueCountFrequency (%)
20019
0.9%
19926
1.3%
19825
1.2%
19719
0.9%
19620
1.0%
19511
0.5%
19416
0.8%
19315
0.8%
19215
0.8%
19115
0.8%

number_of_cores
Real number (ℝ≥0)

Number of cores of processor

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5205
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:37.023288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.287836718
Coefficient of variation (CV)0.5061025811
Kurtosis-1.229749767
Mean4.5205
Median Absolute Deviation (MAD)2
Skewness0.003627508314
Sum9041
Variance5.234196848
MonotocityNot monotonic
2021-05-02T10:57:37.103893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4274
13.7%
7259
13.0%
8256
12.8%
2247
12.3%
5246
12.3%
3246
12.3%
1242
12.1%
6230
11.5%
ValueCountFrequency (%)
1242
12.1%
2247
12.3%
3246
12.3%
4274
13.7%
5246
12.3%
6230
11.5%
7259
13.0%
8256
12.8%
ValueCountFrequency (%)
8256
12.8%
7259
13.0%
6230
11.5%
5246
12.3%
4274
13.7%
3246
12.3%
2247
12.3%
1242
12.1%

primary_cam_resolution
Real number (ℝ≥0)

ZEROS

Primary Camera mega pixels

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9165
Minimum0
Maximum20
Zeros101
Zeros (%)5.1%
Memory size15.8 KiB
2021-05-02T10:57:37.207334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.064314941
Coefficient of variation (CV)0.6115378351
Kurtosis-1.171498795
Mean9.9165
Median Absolute Deviation (MAD)5
Skewness0.01730615047
Sum19833
Variance36.77591571
MonotocityNot monotonic
2021-05-02T10:57:37.303520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10122
 
6.1%
7119
 
5.9%
9112
 
5.6%
20110
 
5.5%
14104
 
5.2%
1104
 
5.2%
0101
 
5.1%
299
 
5.0%
1799
 
5.0%
695
 
4.8%
Other values (11)935
46.8%
ValueCountFrequency (%)
0101
5.1%
1104
5.2%
299
5.0%
393
4.7%
495
4.8%
559
2.9%
695
4.8%
7119
5.9%
889
4.5%
9112
5.6%
ValueCountFrequency (%)
20110
5.5%
1983
4.2%
1882
4.1%
1799
5.0%
1688
4.4%
1592
4.6%
14104
5.2%
1385
4.2%
1290
4.5%
1179
4.0%

px_height
Real number (ℝ≥0)

Pixel Resolution Height

Distinct1137
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.108
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Memory size15.8 KiB
2021-05-02T10:57:37.425363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.95
Q1282.75
median564
Q3947.25
95-th percentile1485.05
Maximum1960
Range1960
Interquartile range (IQR)664.5

Descriptive statistics

Standard deviation443.7808108
Coefficient of variation (CV)0.6879170787
Kurtosis-0.3158654936
Mean645.108
Median Absolute Deviation (MAD)318
Skewness0.6662712561
Sum1290216
Variance196941.408
MonotocityNot monotonic
2021-05-02T10:57:37.540458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3477
 
0.4%
1796
 
0.3%
3716
 
0.3%
2756
 
0.3%
5265
 
0.2%
3275
 
0.2%
6745
 
0.2%
6675
 
0.2%
3565
 
0.2%
565
 
0.2%
Other values (1127)1945
97.2%
ValueCountFrequency (%)
02
0.1%
11
 
0.1%
21
 
0.1%
32
0.1%
43
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
82
0.1%
91
 
0.1%
ValueCountFrequency (%)
19601
0.1%
19491
0.1%
19201
0.1%
19141
0.1%
19011
0.1%
18991
0.1%
18951
0.1%
18781
0.1%
18741
0.1%
18691
0.1%

px_width
Real number (ℝ≥0)

Pixel Resolution Width

Distinct1109
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251.5155
Minimum500
Maximum1998
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:37.666171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile579.85
Q1874.75
median1247
Q31633
95-th percentile1929.05
Maximum1998
Range1498
Interquartile range (IQR)758.25

Descriptive statistics

Standard deviation432.1994469
Coefficient of variation (CV)0.3453408663
Kurtosis-1.186005229
Mean1251.5155
Median Absolute Deviation (MAD)376
Skewness0.01478747377
Sum2503031
Variance186796.3619
MonotocityNot monotonic
2021-05-02T10:57:38.090694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8747
 
0.4%
12477
 
0.4%
13836
 
0.3%
14696
 
0.3%
14636
 
0.3%
14295
 
0.2%
17265
 
0.2%
19235
 
0.2%
12345
 
0.2%
12635
 
0.2%
Other values (1099)1943
97.2%
ValueCountFrequency (%)
5002
0.1%
5012
0.1%
5031
 
0.1%
5061
 
0.1%
5074
0.2%
5081
 
0.1%
5092
0.1%
5103
0.1%
5112
0.1%
5122
0.1%
ValueCountFrequency (%)
19981
 
0.1%
19971
 
0.1%
19961
 
0.1%
19953
0.1%
19942
 
0.1%
19921
 
0.1%
19911
 
0.1%
19901
 
0.1%
19893
0.1%
19885
0.2%

ram
Real number (ℝ≥0)

Random Access Memory in Mega Bytes

Distinct1562
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2124.213
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:38.221259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile445
Q11207.5
median2146.5
Q33064.5
95-th percentile3826.35
Maximum3998
Range3742
Interquartile range (IQR)1857

Descriptive statistics

Standard deviation1084.732044
Coefficient of variation (CV)0.5106512594
Kurtosis-1.19191307
Mean2124.213
Median Absolute Deviation (MAD)932.5
Skewness0.006628035399
Sum4248426
Variance1176643.606
MonotocityNot monotonic
2021-05-02T10:57:38.339542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26104
 
0.2%
22274
 
0.2%
31424
 
0.2%
14644
 
0.2%
12294
 
0.2%
3153
 
0.1%
19583
 
0.1%
12773
 
0.1%
17243
 
0.1%
37033
 
0.1%
Other values (1552)1965
98.2%
ValueCountFrequency (%)
2561
0.1%
2582
0.1%
2591
0.1%
2621
0.1%
2631
0.1%
2651
0.1%
2671
0.1%
2731
0.1%
2771
0.1%
2782
0.1%
ValueCountFrequency (%)
39981
0.1%
39961
0.1%
39931
0.1%
39912
0.1%
39901
0.1%
39841
0.1%
39781
0.1%
39711
0.1%
39702
0.1%
39691
0.1%

screen_height
Real number (ℝ≥0)

Screen Height of mobile in cm

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.3065
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:38.457409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.213245004
Coefficient of variation (CV)0.3423593227
Kurtosis-1.190791247
Mean12.3065
Median Absolute Deviation (MAD)4
Skewness-0.09888424098
Sum24613
Variance17.75143347
MonotocityNot monotonic
2021-05-02T10:57:38.543593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17193
 
9.7%
12157
 
7.8%
7151
 
7.5%
16143
 
7.1%
14143
 
7.1%
15135
 
6.8%
13131
 
6.6%
11126
 
6.3%
10125
 
6.2%
19124
 
6.2%
Other values (5)572
28.6%
ValueCountFrequency (%)
597
4.9%
6114
5.7%
7151
7.5%
8117
5.9%
9124
6.2%
10125
6.2%
11126
6.3%
12157
7.8%
13131
6.6%
14143
7.1%
ValueCountFrequency (%)
19124
6.2%
18120
6.0%
17193
9.7%
16143
7.1%
15135
6.8%
14143
7.1%
13131
6.6%
12157
7.8%
11126
6.3%
10125
6.2%

screen_weight
Real number (ℝ≥0)

ZEROS

Screen Width of mobile in cm

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.767
Minimum0
Maximum18
Zeros180
Zeros (%)9.0%
Memory size15.8 KiB
2021-05-02T10:57:38.648612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.356397606
Coefficient of variation (CV)0.7554010067
Kurtosis-0.3895227894
Mean5.767
Median Absolute Deviation (MAD)3
Skewness0.6337870734
Sum11534
Variance18.9782001
MonotocityNot monotonic
2021-05-02T10:57:38.749691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1210
10.5%
3199
10.0%
4182
9.1%
0180
9.0%
5161
 
8.1%
2156
 
7.8%
7132
 
6.6%
6130
 
6.5%
8125
 
6.2%
10107
 
5.3%
Other values (9)418
20.9%
ValueCountFrequency (%)
0180
9.0%
1210
10.5%
2156
7.8%
3199
10.0%
4182
9.1%
5161
8.1%
6130
6.5%
7132
6.6%
8125
6.2%
997
4.9%
ValueCountFrequency (%)
188
 
0.4%
1719
 
0.9%
1629
 
1.5%
1531
 
1.6%
1433
 
1.7%
1349
2.5%
1268
3.4%
1184
4.2%
10107
5.3%
997
4.9%

talk_time
Real number (ℝ≥0)

longest time that a single battery charge will last when you are

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.011
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T10:57:38.859142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.463955198
Coefficient of variation (CV)0.4962269728
Kurtosis-1.218590963
Mean11.011
Median Absolute Deviation (MAD)5
Skewness0.009511762222
Sum22022
Variance29.8548064
MonotocityNot monotonic
2021-05-02T10:57:38.951226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7124
 
6.2%
4123
 
6.2%
16116
 
5.8%
15115
 
5.8%
19113
 
5.7%
6111
 
5.5%
10105
 
5.2%
8104
 
5.2%
11103
 
5.1%
20102
 
5.1%
Other values (9)884
44.2%
ValueCountFrequency (%)
299
5.0%
394
4.7%
4123
6.2%
593
4.7%
6111
5.5%
7124
6.2%
8104
5.2%
9100
5.0%
10105
5.2%
11103
5.1%
ValueCountFrequency (%)
20102
5.1%
19113
5.7%
18100
5.0%
1798
4.9%
16116
5.8%
15115
5.8%
14101
5.1%
13100
5.0%
1299
5.0%
11103
5.1%

has_three_g
Boolean

Has 3G or not

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1523 
False
477 
ValueCountFrequency (%)
True1523
76.1%
False477
 
23.8%
2021-05-02T10:57:39.035792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

has_touch_screen
Boolean

Has touch screen or not, 1 = yes, 0 = no

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1006 
False
994 
ValueCountFrequency (%)
True1006
50.3%
False994
49.7%
2021-05-02T10:57:39.087027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

has_wifi
Boolean

Has wifi or not

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1014 
False
986 
ValueCountFrequency (%)
True1014
50.7%
False986
49.3%
2021-05-02T10:57:39.139855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

is_expensive
Boolean

This is the target variable with indicating if the mobile phone got a high price. 1 = yes, 0 = no

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1500 
True
500 
ValueCountFrequency (%)
False1500
75.0%
True500
 
25.0%
2021-05-02T10:57:39.192641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2021-05-02T10:57:03.875459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:04.012538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:04.147470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:04.291531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:04.428759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:04.593714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:04.747786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:04.910682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:05.071680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:05.230747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:05.367741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:05.504176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:05.640846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:06.457327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:06.601174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:06.741517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:06.867614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:07.004683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:07.137685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:07.264236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:07.399757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:07.546826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:07.693147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:07.838894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:07.979705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:08.120562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:08.263467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:08.422819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:08.574714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:08.758465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:08.901465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:09.055469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:09.198466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:09.314468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:09.455656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:09.605684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:09.733808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:09.862567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:09.986964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:10.114050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:10.233130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:10.359891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:10.483795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:10.628200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:10.922033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:11.062085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:11.203087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:11.325081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:11.458296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:11.589950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:11.731963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:11.887201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:12.017236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:12.154230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:12.285277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:12.449367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:12.595823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:12.745709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:12.908590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:13.047575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:13.188263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:13.311489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:13.452072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:13.592562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:13.740056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:13.913971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:14.045690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:14.228142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:14.426139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:14.567731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:14.710361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:14.837207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:14.958205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:15.071739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:15.243733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:15.383261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:15.519275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:15.651499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:15.790778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:15.912898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:16.033834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:16.157996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:16.273735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:16.552238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:16.673805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:16.803804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:16.923854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:17.039131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:17.162696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:17.279247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:17.389730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:17.511050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:17.635529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:17.759950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:17.881182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:18.002960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:18.120426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:18.245437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:18.366158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:18.505210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:18.640560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:18.761181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:18.897366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:19.028388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:19.146108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:19.277602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:19.410809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:19.543880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:19.673160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:19.799197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:19.921211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:20.047050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:20.170335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:20.301771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:20.431093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:20.553331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:20.696144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:20.821927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:20.945146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:21.072810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:21.200248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:21.333774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:21.466529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:21.598834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:21.737410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:21.869089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:22.001261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:22.132249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:22.262946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:22.572931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:22.703910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:22.832664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:22.953245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:23.087502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:23.214979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:23.350530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:23.479958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:23.611861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:23.740091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:23.900249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:24.052978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:24.191993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:24.321127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:24.439455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:24.567938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:24.711679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:24.853364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:25.002209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:25.155199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:25.289997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:25.445593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:25.594588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:25.762741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:25.909631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:26.054915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:26.197712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:26.368624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:26.518159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:26.647010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:26.777621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:26.914583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:27.073514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:27.211662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:27.359524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:27.496774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:27.646327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:27.765313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:27.889780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:28.014483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:28.168768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:28.297934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:28.422928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:28.550927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:28.676926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:28.798448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:28.914265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:29.039571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:29.174304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:29.325750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:29.471849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:29.620388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:29.760389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:29.919925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:30.058767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:30.191920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:30.704935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:30.882924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:31.029917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:31.171625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:31.297267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:31.443379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:31.605436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:31.755429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:31.882809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:32.011523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:32.133160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:32.283759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:32.428611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:32.556755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:32.678798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:32.807338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:32.931423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:33.053712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:33.208973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:33.355370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:33.488703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:33.622466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:33.748000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:33.880965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T10:57:34.031238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-02T10:57:39.287181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-02T10:57:39.641360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-02T10:57:39.985873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-02T10:57:40.317648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-02T10:57:40.631173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-02T10:57:34.284933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-02T10:57:34.826514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idbattery_powerhas_bluetoothclock_speedhas_dual_simfront_cam_resolutionhas_four_gint_memorymobile_depthmobile_wtnumber_of_coresprimary_cam_resolutionpx_heightpx_widthramscreen_heightscreen_weighttalk_timehas_three_ghas_touch_screenhas_wifiis_expensive
00842False2.2False1False70.6188222075625499719FalseFalseTrueFalse
111021True0.5True0True530.713636905198826311737TrueTrueFalseFalse
22563True0.5True2True410.9145561263171626031129TrueTrueFalseFalse
33615True2.5False0False100.81316912161786276916811TrueFalseFalseFalse
441821True1.2False13True440.61412141208121214118215TrueTrueFalseFalse
551859False0.5True3False220.71641710041654106717110TrueFalseFalseFalse
661821False1.7False4True100.81398103811018322013818TrueFalseTrueTrue
771954False0.5True0False240.81874051211497001635TrueTrueTrueFalse
881445True0.5False0False530.7174714386836109917120TrueFalseFalseFalse
99509True0.6True2True90.19351511371224513191012TrueFalseFalseFalse

Last rows

idbattery_powerhas_bluetoothclock_speedhas_dual_simfront_cam_resolutionhas_four_gint_memorymobile_depthmobile_wtnumber_of_coresprimary_cam_resolutionpx_heightpx_widthramscreen_heightscreen_weighttalk_timehas_three_ghas_touch_screenhas_wifiis_expensive
199019901617True2.4False8True360.885197431426296537TrueFalseFalseFalse
199119911882False2.0False11True440.81138194743357919820TrueTrueFalseTrue
19921992674True2.9True1False210.21983457618091180634TrueTrueTrueFalse
199319931467True0.5False0False180.6122508881099396215115TrueTrueTrueTrue
19941994858False2.2False1False500.184125281416397817163TrueTrueFalseTrue
19951995794True0.5True0True20.81066141222189066813419TrueTrueFalseFalse
199619961965True2.6True0False390.21874391519652032111016TrueTrueTrueFalse
199719971911False0.9True1True360.71088386816323057915TrueTrueFalseTrue
199819981512False0.9False4True460.114555336670869181019TrueTrueTrueFalse
19991999510True2.0True5True450.916861648375439191942TrueTrueTrueTrue